This adds a visualization to the result report that includes the sampling rate over the whole dataset for the IMU.
This can allow insight into the "quality" of the IMU and if there are any timestamp problems.
For example, the following IMU has incorrect timestamps where a burst of IMU are send at a rate of 1ms with gaps of 6ms between.
A "good" timestamp would be on that has a fixed rate (aside from sensor drops) such as this one which has a fixed 5ms (200Hz) between readings:
While other IMUs can have "jitter" where alternating readings have small errors around the fixed frequency.
This isn't as big of a problem, but is a source of error (not as bad as having the first example)
This adds a visualization to the result report that includes the sampling rate over the whole dataset for the IMU. This can allow insight into the "quality" of the IMU and if there are any timestamp problems. For example, the following IMU has incorrect timestamps where a burst of IMU are send at a rate of 1ms with gaps of 6ms between.![image](https://user-images.githubusercontent.com/2222562/203875223-da6ad767-5757-450f-9795-c1d664eb9de4.png)
A "good" timestamp would be on that has a fixed rate (aside from sensor drops) such as this one which has a fixed 5ms (200Hz) between readings:![image](https://user-images.githubusercontent.com/2222562/203875316-1da84574-1017-4840-8a24-660be22ac36f.png)
While other IMUs can have "jitter" where alternating readings have small errors around the fixed frequency. This isn't as big of a problem, but is a source of error (not as bad as having the first example)![image](https://user-images.githubusercontent.com/2222562/203875390-041154a1-a46d-4eb6-b2ba-345d7d7d69a3.png)